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It’s true: An increasing number of enterprises are looking to replace older-generation MySQL with other database storage processors that are designed specifically for working with massive data sets and time- sensitive processing. This means the future of the original MySQL in the enterprise is questionable.
Ideally, moving applications to the cloud means businesses are able to innovate, grow and change directions at a moment’s notice. But it’s not quite that simple, because many IT systems have rigid underlying MySQL databases that are holding them back. Incapable of keeping up with the cloud’s speed, scalability and flexibility benefits, MySQL frankly is jeopardizing the cloud’s value in application-centric business environments.
A few new-gen companies are moving into this market, seeing a wide-open opportunity. With its frontline Deep Engine, Boston-based startup Deep Information Sciences is one of them.
Earlier this year, the company introduced the Deep Engine storage engine, which is designed to maximize the performance of MySQL and extend that open source database to large-scale data operations, such as real time analytics and big data management.
Deep Engine’s self-tuning storage algorithms can adapt to new hardware deployments and take full advantage of improvements in hardware as systems are upgraded or replaced. With that in mind, it becomes evident that there is more to the Deep Engine than simply being an alternative storage engine.
For those who aren’t quite ready to forklift out their MySQL database just yet, Deep can help with that, too. Deep Engine replaces MySQL’s native storage engines, such as InnoDB or MyISAM, and brings machine-learning metrics to MySQL.
This boosts performance and enables enterprises to better utilize investments in existing MySQL implementations without costly migrations to new hardware or adopting other databases, the company said.
Deep’s been busy with other products. On Sept. 9, the company released a new version of DeepSQL, a machine learning-backed relational database that adapts to host and data conditions in the cloud. Deep claims DeepSQL can outperform Amazon’s MySQL RDS by a factor of 17 and Amazon Aurora by a factor of 5, in terms of processing speed.
DeepSQL is designed to handle varying data demands in cloud systems, Chief Strategy Officer Chad Jones told eWEEK. Because it doesn’t require any application changes, Jones said, DeepSQL eliminates typical MySQL barriers to harvesting some of the main benefits of cloud computing. For example, DeepSQL enables enterprises the option of using Amazon, but at a much lower price point, and with improved performance, Jones said.
“DeepSQL is a NewSQL solution that was architected specifically to shatter MySQL’s performance and scalability barriers without requiring any application change,” Jones said. “It’s an ideal fit for companies that want all the flexibility of Amazon with better cost metrics, a highly performant MySQL database and a flexible EC2 approach. DeepSQL automatically adapts for ever-changing data workload requirements in the cloud.”
DeepSQL, according to Deep Information Sciences, can:
–self-configure, according to the ebbs and flows of the data and the available cloud resources;
–dynamically size and scale up or down, and spin up temporary instances in minutes;
–fully utilize all cloud and virtual instance resources;
–reduce IOPs for a given workload by 80 percent, due to enhanced performance for all disk types, including network attached, SSD and HDD;
–reduce storage footprint by up to 90 percent without sacrificing performance, using out-of-line data compression; and,
–boost overall system performance by optimizing the scheduling of database work. This is driven by enhancements in CPU concurrency and minimizing switches between user and kernel space.